mlsanityData to 7 Oct 2026

Diverse Few-Shot Text Classification with Multiple Metrics

arXiv 1805.07513 ↗First posted 19 May 2018cs.CL cs.LGcited by 61

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Abstract (arXiv)

We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low inter-task variance, we consider a more realistic setting, where tasks are diverse. However, it imposes tremendous difficulties to existing state-of-the-art metric-based algorithms since a single metric is insufficient to capture complex task variations in natural language domain. To alleviate the problem, we propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. Extensive quantitative evaluations on real-world sentiment analysis and dialog intent classification datasets demonstrate that the proposed method performs favorably against state-of-the-art few shot learning algorithms in terms of predictive accuracy. We make our code and data available for further study.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2018.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 20; 2022: 17; 2023: 13; 2024: 7; 2025: 2; 2026: 1.

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  1. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
  2. 2021Trustworthy AI: From Principles to Practicescited by 206
  3. 2021Meta-learning via Language Model In-context Tuningcited by 163
  4. 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
  5. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  6. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  7. 2018Multi-Source Domain Adaptation with Mixture of Expertscited by 62
  8. 2021Discrete and Soft Prompting for Multilingual Modelscited by 49
  9. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
  10. 2023Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attackscited by 32
  11. 2021Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and Systemcited by 30
  12. 2021Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemescited by 27
38 more
  1. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  2. 2021Revisiting Self-Training for Few-Shot Learning of Language Modelcited by 22
  3. 2022Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learningcited by 22
  4. 2022Label Semantics for Few Shot Named Entity Recognitioncited by 21
  5. 2022Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropizationcited by 19
  6. 2022Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mindcited by 19
  7. 2021Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classificationcited by 18
  8. 2023ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classificationcited by 17
  9. 2023CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Modelscited by 16
  10. 2024Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settingscited by 14
  11. 2022Semantic-aligned Fusion Transformer for One-shot Object Detectioncited by 13
  12. 2022Cluster & Tune: Boost Cold Start Performance in Text Classificationcited by 13
  13. 2022Few-Shot Learning on Graphscited by 11
  14. 2023Model-Agnostic Meta-Learning for Multilingual Hate Speech Detectioncited by 11
  15. 2024A Comprehensive Review of Few-shot Action Recognitioncited by 11
  16. 2021Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLPcited by 10
  17. 2021An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Fillingcited by 9
  18. 2021ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classificationcited by 8
  19. 2021ProtoTransformer: A Meta-Learning Approach to Providing Student Feedbackcited by 8
  20. 2022Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representationcited by 8
  21. 2022Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitationcited by 8
  22. 2023A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrievalcited by 8
  23. 2023MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapterscited by 7
  24. 2021A Neural Few-Shot Text Classification Reality Checkcited by 6
  25. 2021Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Fillingcited by 6
  26. 2022Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learningcited by 6
  27. 2023Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Taskscited by 5
  28. 2023Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-trainingcited by 5
  29. 2021ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detectioncited by 4
  30. 2024Domain Adaptation in Intent Classification Systems: A Reviewcited by 4
  31. 2022Similarity Learning based Few Shot Learning for ECG Time Series Classificationcited by 3
  32. 2022Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classificationcited by 3
  33. 2023TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformationcited by 3
  34. 2021Embedding Adaptation is Still Needed for Few-Shot Learningcited by 2
  35. 2021Meta-Learning with Variational Semantic Memory for Word Sense Disambiguationcited by 2
  36. 2022Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clusteringcited by 2
  37. 2023Meta-training with Demonstration Retrieval for Efficient Few-shot Learningcited by 2
  38. 2023Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphscited by 2

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